DATA & TEXT MINING (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: LO1 – Define data and text mining concepts and techniques; LO2 – Explain collection of data and techniques for pre-processing the data before mining; LO3 – Design the data and text mining models to solve problems by extracting knowledge from data; LO4 – Analyze the implementation of data and text mining techniques which appropriate to the need.

Topics:

1. Cluster Analysis: Basic Concepts and Methods;
2. Classification: Basic Concepts – Bayes Classification Methods;
3. Clustering;
4. Introduction of Text Mining;
5. Classification: Basic Concepts – Rule-Based Classification;
6. Information Extraction;
7. Introduction of Data Mining;
8. Categorization;
9. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods;
10. Pre-processing applications using probabilistic and hybrid approaches;
11. Review : Text Mining;
12. Classification: Basic Concepts – Decision Tree Induction;
13. Text mining pre-processing Technique;
14. Review : Data Mining;
15. Getting to Know Your Data;
16. Data Pre-processing.